Information extraction

Automatically pulls out structured information from an unstructured or semi-structured data type to create new structured data using tasks such as entity recognition, relationship extraction and coreference resolution.

Uses predefined concepts to extract common entities, such as names, organizations, locations, expressions of time, dates, quantities, percentages and more.

Conditional Random Field and Probabilistic Semantics are used to label and sequence data and can automated entity and relationship extraction by learning the contextual rules of a given entity. Automatic rule builders promote topics to categories with supervised machine learning.

Sentiment analysis

Identifies and analyze terms, phrases and character strings that imply sentiment.

Visually depicts sentiment through sentiment indicator display at a document or topic level.

Provides ability to use recurrent neural networks for more accurate sentiment classification.

Flexible deployment

Score code is natively threaded for distributed processing, taking maximum advantage of computing resources to reduce latency to results, even on very large data sets.

Analytic store (ASTORE) is a binary file that represents the scoring logic from a specific model or algorithm. That compact asset allows for easy score code movement and integration into existing applications frameworks. ASTORE support is available for the Concepts, Sentiment and Categories nodes.